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Auflistung nach Autor:in "Hassani, Marwan"

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  • Konferenzbeitrag
    Optimizing Sequential Pattern Mining Within Multiple Streams
    (Datenbanksysteme für Business, Technologie und Web (BTW 2015) - Workshopband, 2015) Töws, Daniel; Hassani, Marwan; Beecks, Christian; Seidl, Thomas
    Analyzing information is recently becoming much more important than ever, as it is produced massively in every area. In the past years, data streams became more and more important and so were algorithms that can mine hidden patterns out of those non static data bases. Those algorithms can also be used to simulate processes and to find important information step by step. The translation of an English text into German is such a process. Linguists try to find characteristic patterns in this process to better understand it. For this purpose, keystrokes and eye movements during the process are tracked. The StrPMiner was designed to mine sequential patterns from this translation data. One dominant algorithm to find sequential patterns is the PrefixSpan. Though it was created for static data bases, lots of data stream algorithms collect batches and use the algorithm to find sequential patterns. This batch approach is a simple solution, but makes it impossible to find patterns in between two consequent batches. The PBuilder is introduced to find sequential patterns with a higher accuracy and is used by the StrPMiner to find patterns.
  • Konferenzbeitrag
    Sequential pattern mining of multimodal streams in the humanities
    (Datenbanksysteme für Business, Technologie und Web (BTW 2015), 2015) Hassani, Marwan; Beecks, Christian; Töws, Daniel; Serbina, Tatiana; Haberstroh, Max; Niemietz, Paula; Jeschke, Sabina; Neumann, Stella; Seidl, Thomas
    Research in the humanities is increasingly attracted by data mining and data management techniques in order to efficiently deal with complex scientific corpora. Particularly, the exploration of hidden patterns within different types of data streams arising from psycholinguistic experiments is of growing interest in the area of translation process research. In order to support psycholinguistic experts in quantitatively discovering the non-self-explanatory behavior of the data, we propose the e-cosmos miner framework for mining, generating and visualizing sequential patterns hidden within multimodal streaming data. The introduced MSS-BE algorithm, based on the PrefixSpan method, searches for sequential patterns within multiple streaming inputs arriving from eye tracking and keystroke logging data recorded during translation tasks. The e-cosmos miner enables psycholinguistic experts to select different sequential patterns as they appear in the translation process, compare the evolving changes of their statistics during the process and track their occurrences within a special simulator.